Razni inputi koji aktiviraju mrežu

Lucent paket sadrži i funkcionalnost da za dani chnanel nađe više od jednog inputa koji ga jako aktivira. Dolje to ilustriramo s par primjera.

Definicije i importovi

pip install --quiet torch-lucent

WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Note: you may need to restart the kernel to use updated packages.

from lucent.optvis.transform import pad, jitter, random_rotate, random_scale
from lucent.optvis import render, param, transform, objectives

import torch
import numpy as np
from lucent.optvis import render, param, transform, objectives

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

import torch.nn as nn
import torch.nn.functional as F

class KittyNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.bn0 = nn.BatchNorm2d(3)
        self.conv1 = nn.Conv2d(3, 9, 3)
        self.pool1 = nn.AvgPool2d(4, 4)
        
        self.conv1_bn = nn.BatchNorm2d(9)
        self.conv2 = nn.Conv2d(9, 16, 3)
        self.pool2 = nn.AvgPool2d(4, 4)
        
        self.conv2_bn = nn.BatchNorm2d(16)
        self.conv3 = nn.Conv2d(16, 25, 3)
        self.pool3 = nn.AvgPool2d(4, 4)
        
        self.conv3_bn = nn.BatchNorm2d(25)
        self.conv4 = nn.Conv2d(25, 36, 3)
        self.pool4 = nn.AvgPool2d(2 , 2)
        
        self.fc = nn.Linear(324, 4)

    def forward(self, x):
        x = self.bn0(x)
        x = self.conv1_bn(self.pool1(F.relu(self.conv1(x))))
        x = self.conv2_bn(self.pool2(F.relu(self.conv2(x))))
        x = self.conv3_bn(self.pool3(F.relu(self.conv3(x))))
        x = self.pool4(F.relu(self.conv4(x)))
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = self.fc(x)
        return x
    
class LongcatNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.bn1 = nn.BatchNorm2d(3)
        self.conv1 = nn.Conv2d(3, 9, 3)
        self.pool1 = nn.MaxPool2d(2, 2)
        
        self.conv2_bn = nn.BatchNorm2d(9)
        self.conv2 = nn.Conv2d(9, 16, 3)
        self.pool2 = nn.MaxPool2d(2, 2)
        
        self.conv3_bn = nn.BatchNorm2d(16)
        self.conv3 = nn.Conv2d(16, 25, 3)
        self.pool3 = nn.MaxPool2d(2, 2)
        
        self.conv4_bn = nn.BatchNorm2d(25)
        self.conv4 = nn.Conv2d(25, 36, 3)
        self.pool4 = nn.MaxPool2d(2, 2)
        
        self.conv5_bn = nn.BatchNorm2d(36)
        self.conv5 = nn.Conv2d(36, 36, 3)
  
        self.conv6_bn = nn.BatchNorm2d(36)
        self.conv6 = nn.Conv2d(36, 49, 3)

        self.conv7_bn = nn.BatchNorm2d(49)
        self.conv7 = nn.Conv2d(49, 49, 3)
        
        self.conv8_bn = nn.BatchNorm2d(49)
        self.conv8 = nn.Conv2d(49, 49, 3)
        
        self.conv9_bn = nn.BatchNorm2d(49)
        self.conv9 = nn.Conv2d(49, 49, 3)
        self.pool9 = nn.MaxPool2d(2, 2)

        self.conv10_bn = nn.BatchNorm2d(49)
        self.conv10 = nn.Conv2d(49, 49, 3)
        self.pool10 = nn.MaxPool2d(2, 2)

        self.fc = nn.Linear(1764, 4)

    def forward(self, x):
        x = self.bn1(x)
        x = self.conv2_bn(self.pool1(F.relu(self.conv1(x))))
        x = self.conv3_bn(self.pool2(F.relu(self.conv2(x))))
        x = self.conv4_bn(self.pool3(F.relu(self.conv3(x))))
        x = self.conv5_bn(self.pool4(F.relu(self.conv4(x))))

        x = self.conv6_bn(F.relu(self.conv5(x)))  
        x = self.conv7_bn(F.relu(self.conv6(x)))
        x = self.conv8_bn(F.relu(self.conv7(x)))
        x = self.conv9_bn(F.relu(self.conv8(x)))
        
        
        x = self.conv10_bn(self.pool9(F.relu(self.conv9(x))))
        x = self.pool10(F.relu(self.conv10(x)))
        
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        
        x = self.fc(x)
        return x

kitty = KittyNet()
longcat = LongcatNet()
inception = torch.hub.load('pytorch/vision:v0.10.0', 'googlenet', pretrained=True)


kitty.load_state_dict(torch.load('saved_models/kitty/epoch_7_batch_5000.pth', map_location=device))
longcat.load_state_dict(torch.load('saved_models/longcat/epoch_7_batch_5000.pth', map_location=device))
inception.load_state_dict(torch.load('saved_models/inception/epoch_7_batch_5000.pth', map_location=device))

Using cache found in /root/.cache/torch/hub/pytorch_vision_v0.10.0

<All keys matched successfully>

kitty.to(device).eval()
longcat.to(device).eval()
inception.to(device).eval()

GoogLeNet(
  (conv1): BasicConv2d(
    (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
  )
  (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
  (conv2): BasicConv2d(
    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
  )
  (conv3): BasicConv2d(
    (conv): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
  )
  (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
  (inception3a): Inception(
    (branch1): BasicConv2d(
      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
    )
    (branch2): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch3): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch4): Sequential(
      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)
      (1): BasicConv2d(
        (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
  )
  (inception3b): Inception(
    (branch1): BasicConv2d(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
    )
    (branch2): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch3): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch4): Sequential(
      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)
      (1): BasicConv2d(
        (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
  )
  (maxpool3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
  (inception4a): Inception(
    (branch1): BasicConv2d(
      (conv): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
    )
    (branch2): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(208, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch3): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch4): Sequential(
      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)
      (1): BasicConv2d(
        (conv): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
  )
  (inception4b): Inception(
    (branch1): BasicConv2d(
      (conv): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
    )
    (branch2): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch3): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch4): Sequential(
      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)
      (1): BasicConv2d(
        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
  )
  (inception4c): Inception(
    (branch1): BasicConv2d(
      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
    )
    (branch2): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch3): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch4): Sequential(
      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)
      (1): BasicConv2d(
        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
  )
  (inception4d): Inception(
    (branch1): BasicConv2d(
      (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
    )
    (branch2): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(144, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch3): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch4): Sequential(
      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)
      (1): BasicConv2d(
        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
  )
  (inception4e): Inception(
    (branch1): BasicConv2d(
      (conv): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
    )
    (branch2): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch3): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch4): Sequential(
      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)
      (1): BasicConv2d(
        (conv): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
  )
  (maxpool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)
  (inception5a): Inception(
    (branch1): BasicConv2d(
      (conv): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
    )
    (branch2): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch3): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch4): Sequential(
      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)
      (1): BasicConv2d(
        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
  )
  (inception5b): Inception(
    (branch1): BasicConv2d(
      (conv): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
    )
    (branch2): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch3): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicConv2d(
        (conv): Conv2d(48, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (branch4): Sequential(
      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)
      (1): BasicConv2d(
        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
  )
  (aux1): None
  (aux2): None
  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
  (dropout): Dropout(p=0.2, inplace=False)
  (fc): Linear(in_features=1024, out_features=1000, bias=True)
)

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# da bilježnice budu manje:
%config InlineBackend.figure_format = 'jpg'

def lucent_show_layer(model, layer, grid_dim,
                      param_f=None, transforms=None,
                      optimizer=None, image_size=128):
    n_row = grid_dim
    n_col = grid_dim
    _, axs = plt.subplots(n_row, n_col, figsize=(19.55, 20))
    axs = axs.flatten()
    for ix, ax in zip(range(n_row*n_col), axs):
        img = render.render_vis(model, f"{layer}:{ix}", param_f=param_f,
                                transforms=transforms, progress=False, show_image=False)[0]
        img = np.reshape(img, (image_size, image_size, 3))
        ax.imshow(img)
        ax.set_xticklabels([])
        ax.set_yticklabels([])
        ax.set_xticks([])
        ax.set_yticks([])
        ax.margins(x=0, y=0, tight=True)

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.show()

Kitty primjeri

batch_param_f = lambda: param.image(128, batch=4)

obj = objectives.channel("conv1", 0) - 5e1 * objectives.diversity("conv1") # here we use a higher weight on the diversity term

_ = render.render_vis(kitty, obj, batch_param_f, show_inline=True)

  0%|          | 0/512 [00:00<?, ?it/s]/usr/local/lib/python3.6/dist-packages/lucent/optvis/render.py:104: UserWarning: Some layers could not be computed because the size of the image is not big enough. It is fine, as long as the noncomputed layers are not used in the objective function(exception details: 'Calculated padded input size per channel: (2 x 2). Kernel size: (3 x 3). Kernel size can't be greater than actual input size')
  "Some layers could not be computed because the size of the "
100%|██████████| 512/512 [00:10<00:00, 47.92it/s]

0

1

2

3

obj = objectives.channel("conv2", 0) - 1e2 * objectives.diversity("conv2") # here we use a higher weight on the diversity term

_ = render.render_vis(kitty, obj, batch_param_f, show_inline=True)

100%|██████████| 512/512 [00:10<00:00, 50.01it/s]

0

1

2

3

obj = objectives.channel("conv3", 0) - 1e3 * objectives.diversity("conv3") # here we use a higher weight on the diversity term

_ = render.render_vis(kitty, obj, batch_param_f, show_inline=True)

100%|██████████| 512/512 [00:10<00:00, 49.08it/s]

0

1

2

3

Longcat primjeri

obj = objectives.channel("conv1", 0) - 1e2 * objectives.diversity("conv1") # here we use a higher weight on the diversity term

_ = render.render_vis(longcat, obj, batch_param_f, show_inline=True)

  0%|          | 0/512 [00:00<?, ?it/s]/usr/local/lib/python3.6/dist-packages/lucent/optvis/render.py:104: UserWarning: Some layers could not be computed because the size of the image is not big enough. It is fine, as long as the noncomputed layers are not used in the objective function(exception details: 'Calculated padded input size per channel: (1 x 1). Kernel size: (3 x 3). Kernel size can't be greater than actual input size')
  "Some layers could not be computed because the size of the "
100%|██████████| 512/512 [00:10<00:00, 48.17it/s]

0

1

2

3

obj = objectives.channel("conv2", 2) - 1e3 * objectives.diversity("conv2") # here we use a higher weight on the diversity term

_ = render.render_vis(longcat, obj, batch_param_f, show_inline=True)

100%|██████████| 512/512 [00:10<00:00, 46.69it/s]

0

1

2

3

obj = objectives.channel("conv3", 2) - 1e3 * objectives.diversity("conv3") # here we use a higher weight on the diversity term

_ = render.render_vis(longcat, obj, batch_param_f, show_inline=True)

100%|██████████| 512/512 [00:11<00:00, 45.24it/s]

0

1

2

3

obj = objectives.channel("conv4", 2) - 1e3 * objectives.diversity("conv4") # here we use a higher weight on the diversity term

_ = render.render_vis(longcat, obj, batch_param_f, show_inline=True)

100%|██████████| 512/512 [00:11<00:00, 43.19it/s]

0

1

2

3

obj = objectives.channel("conv5", 0) - 1e3 * objectives.diversity("conv5") # here we use a higher weight on the diversity term

_ = render.render_vis(longcat, obj, batch_param_f, show_inline=True)

100%|██████████| 512/512 [00:12<00:00, 40.86it/s]

0

1

2

3

obj = objectives.channel("conv6", 0) - 1e3 * objectives.diversity("conv6") # here we use a higher weight on the diversity term

_ = render.render_vis(longcat, obj, batch_param_f, show_inline=True)

100%|██████████| 512/512 [00:12<00:00, 41.69it/s]

0

1

2

3

obj = objectives.channel("conv7", 1) - 1e3 * objectives.diversity("conv7") # here we use a higher weight on the diversity term

_ = render.render_vis(longcat, obj, batch_param_f, show_inline=True)

100%|██████████| 512/512 [00:12<00:00, 41.55it/s]

0

1

2

3

obj = objectives.channel("conv8", 1) - 1e3 * objectives.diversity("conv8") # here we use a higher weight on the diversity term

_ = render.render_vis(longcat, obj, batch_param_f, show_inline=True)

100%|██████████| 512/512 [00:12<00:00, 41.38it/s]

0

1

2

3

obj = objectives.channel("conv9", 1) - 5e2 * objectives.diversity("conv9") # here we use a higher weight on the diversity term

_ = render.render_vis(longcat, obj, batch_param_f, show_inline=True)

100%|██████████| 512/512 [00:12<00:00, 40.54it/s]

0

1

2

3

Inception

batch_param_f = lambda: param.image(256, fft=True, batch=4)

obj = objectives.channel("inception4a", 97) - 1e2 * objectives.diversity("inception4a") # here we use a higher weight on the diversity term

_ = render.render_vis(inception, obj, batch_param_f, show_inline=True)

100%|██████████| 512/512 [00:16<00:00, 31.03it/s]

0

1

2

3

batch_param_f = lambda: param.image(256, fft=True, batch=4)

obj = objectives.channel("inception4a", 98) - 1e2 * objectives.diversity("inception4a") # here we use a higher weight on the diversity term

_ = render.render_vis(inception, obj, batch_param_f, show_inline=True)

100%|██████████| 512/512 [00:16<00:00, 30.90it/s]

0

1

2

3

batch_param_f = lambda: param.image(256, fft=True, batch=4)

obj = objectives.channel("inception4b", 98) - 1e2 * objectives.diversity("inception4a") # here we use a higher weight on the diversity term

_ = render.render_vis(inception, obj, batch_param_f, show_inline=True)

100%|██████████| 512/512 [00:17<00:00, 28.55it/s]

0

1

2

3